Analysis of eigendecomposition for sets of correlated images at different resolutions

Abstract

Eigendecomposition is a common technique that is performed on sets of correlated images in a number of computer vision and robotics applications. Unfortunately, the computation of an eigendecomposition becomes prohibitively expensive when dealing with very high resolution images. Reducing the resolution of the images reduces the computational expense, it is not known how this affects the quality of the resulting eigendecomposition. The work presented here gives the theoretical background for quantifying the effects of varying the resolution of images on the eigendecomposition that is computed from those images. A computationally efficient algorithm for this eigendecomposition is proposed using derived analytical expressions. Examples show that this algorithm performs very well on arbitrary video sequences.